ABSTRACT Misuse of psychostimulants, such as cocaine and amphetamines, poses a serious public health problem with an estimated 1.3 million users meeting diagnostic criterion for a psychostimulant use disorder (StUD). Currently, there are no pharmacotherapies approved to treat StUDs and existing psychosocial interventions have limited success. Moreover, less than 20% of individuals with a StUD receive treatment, resulting in a substantial population with untreated or under-treated StUDs. The goal of this proposal is to address this unmet clinical need through observational and exploratory analyses to identify pharmacotherapies that could be repurposed to treat StUDs. Application of big data analytics to health claims data is emerging as a tool to inform biomedical research, policy, and clinical practice, including guiding the development of hypotheses for drug discovery and clinical trials. Big data analytics will be used to conduct hypothesis-driven and data-driven analyses of the Truven Health MarketScan database to evaluate the potential of prescribed medications to be repurposed for StUDs. In Aim 1, a retrospective cohort study will be used to test the hypothesis that, among individuals with StUD, exposure to antidepressants increases the likelihood of StUD remission. Aim 2 will leverage the power of big data to discover complex relationships between prescribed medications and StUD remission. Specifically, an association rule mining algorithm will be used to generate knowledge of therapeutics that may be beneficial for the treatment of StUDs. Achievement of the proposed aims will improve the understanding of StUDs, provide insights for the design of future randomized clinical trials, and identify novel biological targets for drug discovery research, thereby advancing translational research efforts to identify pharmacotherapies to treat StUDs.